Prevalence and Predictors of Barrett's Esophagus After Negative Initial Endoscopy: Analysis From Two National Databases.

BACKGROUND & AIMS: Guidelines suggest a single screening esophagogastroduodenoscopy (EGD) in patients with multiple risk factors for Barrett's esophagus (BE). We aimed to determine BE prevalence and predictors on repeat EGD after a negative initial EGD, using 2 large national databases (GI Quality Improvement Consortium [GIQuIC] and TriNetX). METHODS: Patients who underwent at least 2 EGDs were included and those with BE or esophageal adenocarcinoma detected at initial EGD were excluded. Patient demographics and prevalence of BE on repeat EGD were collected. Multivariate logistic regression was performed to assess for independent risk factors for BE detected on the repeat EGD. RESULTS: In 214,318 and 153,445 patients undergoing at least 2 EGDs over a median follow-up of 28-35 months, the prevalence of BE on repeat EGD was 1.7% in GIQuIC and 3.4% in TriNetX, respectively (26%-45% of baseline BE prevalence). Most (89%) patients had nondysplastic BE. The prevalence of BE remained stable over time (from 1 to >5 years from negative initial EGD) but increased with increasing number of risk factors. BE prevalence in a high-risk population (gastroesophageal reflux disease plus ≥1 risk factor for BE) was 3%-4%. CONCLUSIONS: In this study of >350,000 patients, rates of BE on repeat EGD ranged from 1.7%-3.4%, and were higher in those with multiple risk factors. Most were likely missed at initial evaluation, underscoring the importance of a high-quality initial endoscopic examination. Although routine repeat endoscopic BE screening after a negative initial examination is not recommended, repeat screening may be considered in carefully selected patients with gastroesophageal reflux disease and ≥2 risk factors for BE, potentially using nonendoscopic tools.

Algorithm Training and Testing for a Nonendoscopic Barrett's Esophagus Detection Test in Prospective Multicenter Cohorts.

BACKGROUND & AIMS: Endoscopic Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC) detection is invasive and expensive. Nonendoscopic BE/EAC detection tools are guideline-endorsed alternatives. We previously described a 5-methylated DNA marker (MDM) panel assayed on encapsulated sponge cell collection device (CCD) specimens. We aimed to train a new algorithm using a 3-MDM panel and test its performance in an independent cohort. METHODS: Algorithm training and test samples were from 2 prospective multicenter cohorts. All BE cases had esophageal intestinal metaplasia (with or without dysplasia/EAC); control subjects had no endoscopic evidence of BE. The CCD procedure was followed by endoscopy. From CCD cell lysates, DNA was extracted, bisulfite treated, and MDMs were blindly assayed. The algorithm was set and locked using cross-validated logistic regression (training set) and its performance was assessed in an independent test set. RESULTS: Training (N = 352) and test (N = 125) set clinical characteristics were comparable. The final panel included 3 MDMs (NDRG4, VAV3, ZNF682). Overall sensitivity was 82% (95% CI, 68%-94%) at 90% (79%-98%) specificity and 88% (78%-94%) sensitivity at 84% (70%-93%) specificity in training and test sets, respectively. Sensitivity was 90% and 68% for all long- and short-segment BE, respectively. Sensitivity for BE with high-grade dysplasia and EAC was 100% in training and test sets. Overall sensitivity for nondysplastic BE was 82%. Areas under the receiver operating characteristic curves for BE detection were 0.92 and 0.94 in the training and test sets, respectively. CONCLUSIONS: A locked 3-MDM panel algorithm for BE/EAC detection using a nonendoscopic CCD demonstrated excellent sensitivity for high-risk BE cases in independent validation samples. (Clinical trials.gov: NCT02560623, NCT03060642.).

Rates of Recurrent Intestinal Metaplasia and Dysplasia After Successful Endoscopic Therapy of Barrett's Neoplasia by Endoscopic Mucosal Resection vs Endoscopic Submucosal Dissection and Ablation: A Large North American Multicenter Cohort.

INTRODUCTION: Endoscopic eradication therapy (EET) combining endoscopic resection (ER) with endoscopic mucosal resection (EMR) or endoscopic submucosal dissection (ESD) followed by ablation is the standard of care for the treatment of dysplastic Barrett's esophagus (BE). We have previously shown comparable rates of complete remission of intestinal metaplasia (CRIM) with both approaches. However, data comparing recurrence after CRIM are lacking. We compared rates of recurrence after CRIM with both techniques in a multicenter cohort. METHODS: Patients undergoing EET achieving CRIM at 3 academic institutions were included. Demographic and clinical data were abstracted. Outcomes included rates and predictors of any BE and dysplastic BE recurrence in the 2 groups. Cox-proportional hazards models and inverse probability treatment weighting (IPTW) analysis were used for analysis. RESULTS: A total of 621 patients (514 EMR and 107 ESD) achieving CRIM were included in the recurrence analysis. The incidence of any BE (15.7, 5.7 per 100 patient-years) and dysplastic BE recurrence (7.3, 5.3 per 100 patient-years) were comparable in the EMR and ESD groups, respectively. On multivariable analyses, the chances of BE recurrence were not influenced by ER technique (hazard ratio 0.87; 95% confidence interval 0.51-1.49; P = 0.62), which was also confirmed by IPTW analysis (ESD vs EMR: hazard ratio 0.98; 95% confidence interval 0.56-1.73; P = 0.94). BE length, lesion size, and history of cigarette smoking were independent predictors of BE recurrence. DISCUSSION: Patients with BE dysplasia/neoplasia achieving CRIM, initially treated with EMR/ablation, had comparable recurrence rates to ESD/ablation. Randomized trials are needed to confirm these outcomes between the 2 ER techniques.

Clinical Outcomes After Endoscopic Management of Low-Risk and High-Risk T1a Esophageal Adenocarcinoma: A Multicenter Study.

INTRODUCTION: Endoscopic eradication therapy (EET) is standard of care for T1a esophageal adenocarcinoma (EAC). However, data on outcomes in high-risk T1a EAC are limited. We assessed and compared outcomes after EET of low-risk and high-risk T1a EAC, including intraluminal EAC recurrence, extraesophageal metastases, and overall survival. METHODS: Patients who underwent EET for T1a EAC at 3 referral Barrett's esophagus endotherapy units between 1996 and 2022 were included. Patients with submucosal invasion, positive deep margins, or metastases at initial diagnosis were excluded. High-risk T1a EAC was defined as T1a EAC with poor differentiation and/or lymphovascular invasion, with low-risk disease being defined without these features. All pathology was systematically assessed by expert gastrointestinal pathologists. Baseline and follow-up endoscopy and pathology data were abstracted. Time-to-event analyses were performed to compare outcomes between groups. RESULTS: One hundred eighty-eight patients with T1a EAC were included (high risk, n = 45; low risk, n = 143) with a median age of 70 years, and 84% were men. Groups were comparable for age, sex, Barrett's esophagus length, lesion size, and EET technique. Rates of delayed extraesophageal metastases (11.1% vs 1.4%) were significantly higher in the high-risk group ( P = 0.02). There was no significant difference in the rates of intraluminal EAC recurrence ( P = 0.79) and overall survival ( P = 0.73) between the 2 groups. DISCUSSION: Patients with high-risk T1a EAC undergoing successful EET had a substantially higher rate of extraesophageal metastases compared with those with low-risk T1a EAC on long-term follow-up. These data should be factored into discussions with patients while selecting treatment approaches. Additional prospective data in this area are critical.

Physician perceptions on the current and future impact of artificial intelligence to the field of gastroenterology.

BACKGROUND AND AIMS: The use of artificial intelligence (AI) has transformative implications to the practice of gastroenterology and endoscopy. The aims of this study were to understand the perceptions of the gastroenterology community toward AI and to identify potential barriers for adoption. METHODS: A 16-question online survey exploring perceptions on the current and future implications of AI to the field of gastroenterology was developed by the American Society for Gastrointestinal Endoscopy AI Task Force and distributed to national and international society members. Participant demographic information including age, sex, experience level, and practice setting was collected. Descriptive statistics were used to summarize survey findings, and a Pearson χ2 analysis was performed to determine the association between participant demographic information and perceptions of AI. RESULTS: Of 10,162 invited gastroenterologists, 374 completed the survey. The mean age of participants was 46 years (standard deviation, 12), and 299 participants (80.0%) were men. One hundred seventy-nine participants (47.9%) had >10 years of practice experience, with nearly half working in the community setting. Only 25 participants (6.7%) reported the current use of AI in their clinical practice. Most participants (95.5%) believed that AI solutions will have a positive impact in their practice. One hundred seventy-six participants (47.1%) believed that AI will make clinical duties more technical but will also ease the burden of the electronic medical record (54.0%). The top 3 areas where AI was predicted to be most influential were endoscopic lesion detection (65.3%), endoscopic lesion characterization (65.8%), and quality metrics (32.6%). Participants voiced a desire for education on topics such as the clinical use of AI applications (64.4%), the advantages and limitations of AI applications (57.0%), and the technical methodology of AI (44.7%). Most participants (42.8%) expressed that the cost of AI implementation should be covered by their hospital. Demographic characteristics significantly associated with this perception included participants' years in practice and practice setting. CONCLUSIONS: Gastroenterologists have an overall positive perception regarding the use of AI in clinical practice but voiced concerns regarding its technical aspects and coverage of costs associated with implementation. Further education on the clinical use of AI applications with understanding of the advantages and limitations appears to be valuable in promoting adoption.

Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology.

BACKGROUND AND AIMS: The American Society for Gastrointestinal Endoscopy (ASGE) AI Task Force along with experts in endoscopy, technology space, regulatory authorities, and other medical subspecialties initiated a consensus process that analyzed the current literature, highlighted potential areas, and outlined the necessary research in artificial intelligence (AI) to allow a clearer understanding of AI as it pertains to endoscopy currently. METHODS: A modified Delphi process was used to develop these consensus statements. RESULTS: Statement 1: Current advances in AI allow for the development of AI-based algorithms that can be applied to endoscopy to augment endoscopist performance in detection and characterization of endoscopic lesions. Statement 2: Computer vision-based algorithms provide opportunities to redefine quality metrics in endoscopy using AI, which can be standardized and can reduce subjectivity in reporting quality metrics. Natural language processing-based algorithms can help with the data abstraction needed for reporting current quality metrics in GI endoscopy effortlessly. Statement 3: AI technologies can support smart endoscopy suites, which may help optimize workflows in the endoscopy suite, including automated documentation. Statement 4: Using AI and machine learning helps in predictive modeling, diagnosis, and prognostication. High-quality data with multidimensionality are needed for risk prediction, prognostication of specific clinical conditions, and their outcomes when using machine learning methods. Statement 5: Big data and cloud-based tools can help advance clinical research in gastroenterology. Multimodal data are key to understanding the maximal extent of the disease state and unlocking treatment options. Statement 6: Understanding how to evaluate AI algorithms in the gastroenterology literature and clinical trials is important for gastroenterologists, trainees, and researchers, and hence education efforts by GI societies are needed. Statement 7: Several challenges regarding integrating AI solutions into the clinical practice of endoscopy exist, including understanding the role of human-AI interaction. Transparency, interpretability, and explainability of AI algorithms play a key role in their clinical adoption in GI endoscopy. Developing appropriate AI governance, data procurement, and tools needed for the AI lifecycle are critical for the successful implementation of AI into clinical practice. Statement 8: For payment of AI in endoscopy, a thorough evaluation of the potential value proposition for AI systems may help guide purchasing decisions in endoscopy. Reliable cost-effectiveness studies to guide reimbursement are needed. Statement 9: Relevant clinical outcomes and performance metrics for AI in gastroenterology are currently not well defined. To improve the quality and interpretability of research in the field, steps need to be taken to define these evidence standards. Statement 10: A balanced view of AI technologies and active collaboration between the medical technology industry, computer scientists, gastroenterologists, and researchers are critical for the meaningful advancement of AI in gastroenterology. CONCLUSIONS: The consensus process led by the ASGE AI Task Force and experts from various disciplines has shed light on the potential of AI in endoscopy and gastroenterology. AI-based algorithms have shown promise in augmenting endoscopist performance, redefining quality metrics, optimizing workflows, and aiding in predictive modeling and diagnosis. However, challenges remain in evaluating AI algorithms, ensuring transparency and interpretability, addressing governance and data procurement, determining payment models, defining relevant clinical outcomes, and fostering collaboration between stakeholders. Addressing these challenges while maintaining a balanced perspective is crucial for the meaningful advancement of AI in gastroenterology.

Population Based Time Trends in the Epidemiology and Mortality of Gastroesophageal Junction and Esophageal Adenocarcinoma.

BACKGROUND: Limited data are available on the epidemiology of gastroesophageal junction adenocarcinoma (GEJAC), particularly in comparison to esophageal adenocarcinoma (EAC). With the advent of molecular non-endoscopic Barrett's esophagus (BE) detection tests which sample the esophagus and gastroesophageal junction, early detection of EAC and GEJAC has become a possibility and their epidemiology has gained importance. AIMS: We sought to evaluate time trends in the epidemiology and survival of patients with EAC and GEJAC in a population-based cohort. METHODS: EAC and GEJAC patients from 1976 to 2019 were identified using ICD 9 and 10 diagnostic codes from the Rochester Epidemiology Project (REP). Clinical data and survival status were abstracted. Poisson regression was used to calculate incidence rate ratios (IRR). Survival analysis and Cox proportional models were used to assess predictors of survival. RESULTS: We included 443 patients (287 EAC,156 GEJAC). The incidence of EAC and GEJAC during 1976-2019 was 1.40 (CI 1.1-1.74) and 0.83 (CI 0.61-1.11) per 100,000 people, respectively. There was an increase in the incidence of EAC (IRR = 2.45, p = 0.011) and GEJAC (IRR = 3.17, p = 0.08) from 2000 to 2004 compared to 1995-1999, plateauing in later time periods. Most patients had associated BE and presented at advanced stages, leading to high 5-year mortality rates (66% in EAC and 59% in GEJAC). Age and stage at diagnosis were predictors of mortality. CONCLUSION: The rising incidence of EAC/GEJAC appears to have plateaued somewhat in the last decade. However, both cancers present at advanced stages with persistently poor survival, underscoring the need for early detection.

Framework and metrics for the clinical use and implementation of artificial intelligence algorithms into endoscopy practice: recommendations from the American Society for Gastrointestinal Endoscopy Artificial Intelligence Task Force.

In the past few years, we have seen a surge in the development of relevant artificial intelligence (AI) algorithms addressing a variety of needs in GI endoscopy. To accept AI algorithms into clinical practice, their effectiveness, clinical value, and reliability need to be rigorously assessed. In this article, we provide a guiding framework for all stakeholders in the endoscopy AI ecosystem regarding the standards, metrics, and evaluation methods for emerging and existing AI applications to aid in their clinical adoption and implementation. We also provide guidance and best practices for evaluation of AI technologies as they mature in the endoscopy space. Note, this is a living document; periodic updates will be published as progress is made and applications evolve in the field of AI in endoscopy.

Long-term outcomes following successful endoscopic treatment of T1 esophageal adenocarcinoma: a multicenter cohort study.

BACKGROUND AND AIMS: Endoscopic eradication therapy (EET) is guideline endorsed for management of early-stage (T1) esophageal adenocarcinoma (EAC). Patients with baseline high-grade dysplasia (HGD) and EAC are at highest risk of recurrence after successful EET, but limited data exist on long-term (>5 year) recurrence outcomes. Our aim was to assess the incidence and predictors of long-term recurrence in a multicenter cohort of patients with T1 EAC treated with EET. METHODS: Patients with T1 EAC achieving successful endoscopic cancer eradication with a minimum of 5 years' clinical follow-up were included. The primary outcome was neoplastic recurrence, defined as dysplasia or EAC, and it was characterized as early (<2 years), intermediate (2-5 years), or late (>5 years). Predictors of recurrence were assessed by time to event analysis. RESULTS: A total of 84 T1 EAC patients (75 T1a, 9 T1b) with a median 9.1 years (range, 5.1-18.3 years) of follow-up were included. The overall incidence of neoplastic recurrence was 2.0 per 100 person-years of follow-up. Seven recurrences (3 dysplasia, 4 EAC) occurred after 5 years of EAC remission. Overall, 88% of recurrences were treated successfully endoscopically. EAC recurrence-related mortality occurred in 3 patients at a median of 5.2 years from EAC remission. Complete eradication of intestinal metaplasia was independently associated with reduced recurrence (hazard ratio, .13). CONCLUSIONS: Following successful EET of T1 EAC, neoplastic recurrence occurred after 5 years in 8.3% of cases. Careful long-term surveillance should be continued in this patient population. Complete eradication of intestinal metaplasia should be the therapeutic end point for EET.

A novel blood-sensing capsule for rapid detection of upper GI bleeding: a prospective clinical trial.

BACKGROUND AND AIMS: Upper GI bleeding (UGIB) is a common medical emergency associated with high resource utilization, morbidity, and mortality. Timely EGD can be challenging from personnel, resource, and access perspectives. PillSense (EnteraSense Ltd, Galway, Ireland) is a novel swallowed bleeding sensor for the detection of UGIB, anticipated to aid in patient triage and guide clinical decision-making for individuals with suspected UGIB. METHODS: This prospective, open-label, single-arm comparative clinical trial of a novel bleeding sensor for patients with suspected UGIB was performed at a tertiary care center. The PillSense system consists of an optical sensor and an external receiver that processes and displays data from the capsule as "Blood Detected" or "No Blood Detected." Patients underwent EGD within 4 hours of capsule administration; participants were followed up for 21 days to confirm capsule passage. RESULTS: A total of 126 patients were accrued to the study (59.5% male; mean age, 62.4 ± 14.3 years). Sensitivity and specificity for detecting the presence of blood were 92.9% (P = .02) and 90.6% (P < .001), respectively. The capsule's positive and negative predictive values were 74.3% and 97.8%, and positive and negative likelihood ratios were 9.9 and .08. No adverse events or deaths occurred related to the PillSense system, and all capsules were excreted from patients on follow-up. CONCLUSIONS: The PillSense system is safe and effective for detecting the presence of blood in patients evaluated for UGIB before upper GI endoscopy. It is a rapidly deployed tool, with easy-to-interpret results that will affect the diagnosis and triage of patients with suspected UGIB. (Clinical trial registration number: NCT05385224.).

Prediction of Progression in Barrett's Esophagus Using a Tissue Systems Pathology Test: A Pooled Analysis of International Multicenter Studies.

BACKGROUND & AIMS: Prediction of progression risk in Barrett's esophagus (BE) may enable personalized management. We aimed to assess the adjunct value of a tissue systems pathology test (TissueCypher) performed on paraffin-embedded biopsy tissue, when added to expert pathology review in predicting incident progression, pooling individual patient-level data from multiple international studies METHODS: Demographics, clinical features, the TissueCypher risk class/score, and progression status were analyzed. Conditional logistical regression analysis was used to develop multivariable models predicting incident progression with and without the TissueCypher risk class (low, intermediate, high). Concordance (c-) statistics were calculated and compared with likelihood ratio tests to assess predictive ability of models. A risk prediction calculator integrating clinical variables and TissueCypher risk class was also developed. RESULTS: Data from 552 patients with baseline no (n = 472), indefinite (n = 32), or low-grade dysplasia (n = 48) (comprising 152 incident progressors and 400 non-progressors) were analyzed. A high-risk test class independently predicted increased risk of progression to high-grade dysplasia/adenocarcinoma (odds ratio, 6.0; 95% confidence interval, 2.9-12.0), along with expert confirmed low-grade dysplasia (odds ratio, 2.9; 95% confidence interval, 1.2-7.2). Model prediction of progression with the TissueCypher risk class incorporated was significantly superior than without, in the whole cohort (c-statistic 0.75 vs 0.68; P < .0001) and the nondysplastic BE subset (c-statistic 0.72 vs 0.63; P < .0001). Sensitivity and specificity of the high risk TissueCypher class were 38% and 94%, respectively. CONCLUSIONS: An objective tissue systems pathology test high-risk class is a strong independent predictor of incident progression in patients with BE, substantially improving progression risk prediction over clinical variables alone. Although test specificity was high, sensitivity was modest.

Optimized Surveillance Intervals Following Endoscopic Eradication of Dysplastic Barrett's Esophagus: An International Cohort Study.

BACKGROUND & AIMS: Recommended surveillance intervals after complete eradication of intestinal metaplasia (CE-IM) after endoscopic eradication therapy (EET) are largely not evidence-based. Using recurrence rates in a multicenter international Barrett's esophagus (BE) CE-IM cohort, we aimed to generate optimal intervals for surveillance. METHODS: Patients with dysplastic BE undergoing EET and achieving CE-IM from prospectively maintained databases at 5 tertiary-care centers in the United States and the United Kingdom were included. The cumulative incidence of recurrence was estimated, accounting for the unknown date of actual recurrence that lies between the dates of current and previous endoscopy. This cumulative incidence of recurrence subsequently was used to estimate the proportion of patients with undetected recurrence for various surveillance intervals over 5 years. Intervals were selected that minimized recurrences remaining undetected for more than 6 months. Actual patterns of post-CE-IM follow-up evaluation are described. RESULTS: A total of 498 patients (with baseline low-grade dysplasia, 115 patients; high-grade dysplasia [HGD], 288 patients; and intramucosal adenocarcinoma [IMCa], 95 patients) were included. Any recurrence occurred in 27.1% and dysplastic recurrence occurred in 8.4% over a median of 2.6 years of follow-up evaluation. For pre-ablation HGD/IMCa, intervals of 6, 12, 18, and 24 months, and then annually, resulted in no patients with dysplastic recurrence undetected for more than 6 months, comparable with current guideline recommendations despite a 33% reduction in the number of surveillance endoscopies. For pre-ablation low-grade dysplasia, intervals of 1, 2, and 4 years balanced endoscopic burden and undetected recurrence risk. CONCLUSIONS: Lengthening post-CE-IM surveillance intervals would reduce the endoscopic burden after CE-IM with comparable rates of recurrent HGD/IMCa. Future guidelines should consider reduced surveillance frequency.

Feasibility and Safety of Tethered Capsule Endomicroscopy in Patients With Barrett's Esophagus in a Multi-Center Study.

BACKGROUND & AIMS: Tethered capsule endomicroscopy (TCE) involves swallowing a small tethered pill that implements optical coherence tomography (OCT) imaging, procuring high resolution images of the whole esophagus. Here, we demonstrate and evaluate the feasibility and safety of TCE and a portable OCT imaging system in patients with Barrett's esophagus (BE) in a multi-center (5-site) clinical study. METHODS: Untreated patients with BE as per endoscopic biopsy diagnosis were eligible to participate in the study. TCE procedures were performed in unsedated patients by either doctors or nurses. After the capsule was swallowed, the device continuously obtained 10-µm-resolution cross-sectional images as it traversed the esophagus. Following imaging, the device was withdrawn through mouth, and disinfected for subsequent reuse. BE lengths were compared to endoscopy findings when available. OCT-TCE images were compared to volumetric laser endomicroscopy (VLE) images from a patient who had undergone VLE on the same day as TCE. RESULTS: 147 patients with BE were enrolled across all sites. 116 swallowed the capsule (79%), 95/114 (83.3%) men and 21/33 (63.6%) women (P = .01). High-quality OCT images were obtained in 104/111 swallowers (93.7%) who completed the procedure. The average imaging duration was 5.55 ± 1.92 minutes. The mean length of esophagus imaged per patient was 21.69 ± 5.90 cm. A blinded comparison of maximum extent of BE measured by OCT-TCE and EGD showed a strong correlation (r = 0.77-0.79). OCT-TCE images were of similar quality to those obtained by OCT-VLE. CONCLUSIONS: The capabilities of TCE to be used across multiple sites, be administered to unsedated patients by either physicians or nurses who are not expert in OCT-TCE, and to rapidly and safely evaluate the microscopic structure of the esophagus make it an emerging tool for screening and surveillance of BE patients. Clinical trial registry website and trial number: NCT02994693 and NCT03459339.

Development of a deep learning model for the histologic diagnosis of dysplasia in Barrett's esophagus.

BACKGROUND AND AIMS: The risk of progression in Barrett's esophagus (BE) increases with development of dysplasia. There is a critical need to improve the diagnosis of BE dysplasia, given substantial interobserver disagreement among expert pathologists and overdiagnosis of dysplasia by community pathologists. We developed a deep learning model to predict dysplasia grade on whole-slide imaging. METHODS: We digitized nondysplastic BE (NDBE), low-grade dysplasia (LGD), and high-grade dysplasia (HGD) histology slides. Two expert pathologists confirmed all histology and digitally annotated areas of dysplasia. Training, validation, and test sets were created (by a random 70/20/10 split). We used an ensemble approach combining a "you only look once" model to identify regions of interest and histology class (NDBE, LGD, or HGD) followed by a ResNet101 model pretrained on ImageNet applied to the regions of interest. Diagnostic performance was determined for the whole slide. RESULTS: We included slides from 542 patients (164 NDBE, 226 LGD, and 152 HGD) yielding 8596 bounding boxes in the training set, 1946 bounding boxes in the validation set, and 840 boxes in the test set. When the ensemble model was used, sensitivity and specificity for LGD was 81.3% and 100%, respectively, and >90% for NDBE and HGD. The overall positive predictive value and sensitivity metric (calculated as F1 score) was .91 for NDBE, .90 for LGD, and 1.0 for HGD. CONCLUSIONS: We successfully trained and validated a deep learning model to accurately identify dysplasia on whole-slide images. This model can potentially help improve the histologic diagnosis of BE dysplasia and the appropriate application of endoscopic therapy.

Comparative outcomes of radiofrequency ablation and cryoballoon ablation in dysplastic Barrett's esophagus: a propensity score-matched cohort study.

BACKGROUND AND AIMS: Strong evidence supports the use of radiofrequency ablation (RFA) in the management of dysplastic/neoplastic Barrett's esophagus (BE). Recently, the efficacy of the cryoballoon ablation (CBA) system was demonstrated in multicenter cohort studies. We aimed to assess the comparative effectiveness and safety of these 2 ablation modalities for endoscopic eradication therapy (EET) in a cohort study. METHODS: Data were abstracted on patients with dysplastic BE or intramucosal carcinoma undergoing EET using RFA or CBA as the primary ablation modality at 2 referral centers. The primary outcome was the rate of complete remission intestinal metaplasia (CRIM). Secondary outcomes were rates of complete remission of dysplasia (CRD) and adverse events. Cox proportional hazards models and propensity scored-matched analyses were conducted to compare outcomes. RESULTS: Three hundred eleven patients (CBA, 85 patients; RFA, 226 patients) with a median follow-up of 1.5 years (interquartile range, .8, 2.5) in the RFA group and 2.0 years (interquartile range, 1.3, 2.5) in the CBA group were studied. On multivariable analyses, the chances of reaching CRD and CRIM were not influenced by ablation modality. Propensity score-matched analysis revealed a comparable chance of achieving CRIM (CBA vs RFA: hazard ratio, 1.24; 95% confidence interval, .79-1.96; P = .35) and CRD (CBA vs RFA: hazard ratio, 1.19; 95% confidence interval, .82-1.73; P = .36). The CBA group had a higher stricture rate compared with the RFA group (10.4% vs 4.4%, P = .04). CONCLUSIONS: Histologic outcomes of EET using CBA and RFA for dysplastic BE appear to be comparable. A randomized trial is needed to definitively compare outcomes between these 2 modalities.

Spray cryotherapy prevents need for palliative stenting in patients with esophageal cancer-associated dysphagia.

BACKGROUND: Dysphagia is the most common symptom in advanced esophageal cancer patients. Esophageal stent placement (SP) is a common palliation method but can be associated with significant morbidity. Limited data exist regarding the ability of spray cryotherapy (SC) prolong time to SP. METHODS: A Mayo Clinic (Rochester, MN) patient database was reviewed for cases with a SC indication of esophageal cancer palliation from 2007-2019. Procedures were performed using a liquid nitrogen SC system to apply 2-5 separate 20 second freeze and 60 second thaw cycles based on tumor characteristics. Primary outcome was time to subsequent palliative SP. RESULTS: Of 56 patients (71.4% male, mean age 77.8 ± 10.2 years) who underwent a total of 199 SC sessions (mean 3.6 ± 2.7, range 1-12 per patient), 41 had adenocarcinoma and 15 squamous cell carcinoma (SCC). Overall, 13 patients underwent subsequent SP within a mean duration of 15.7 ± 11.0 months over a mean follow-up duration of 25.6 ± 29.4 months. Treatment did produce stenosis in 16 patients, who required dilation within a mean period of 193.1 ± 294.1 days; notably, 10 patients had a history of preceding malignant strictures requiring dilation. Two patients experienced bleeding requiring transfusion, whereas 1 experienced perforation at the start of SC. Prior chemotherapy and/or radiation was not associated with developing an SC-related complication (risk ratio (RR) 1.5; 95% CI 0.6-3.7, P > 0.4). CONCLUSIONS: SC appears to be an effective and safe modality to palliate esophageal cancer in appropriate candidates. Majority of patients who undergo SC avoid the need for future SP. If patients eventually require SP, they are able to, on average, defer stenting for >1 year from SC initiation.

Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis.

OBJECTIVE: The diagnosis of autoimmune pancreatitis (AIP) is challenging. Sonographic and cross-sectional imaging findings of AIP closely mimic pancreatic ductal adenocarcinoma (PDAC) and techniques for tissue sampling of AIP are suboptimal. These limitations often result in delayed or failed diagnosis, which negatively impact patient management and outcomes. This study aimed to create an endoscopic ultrasound (EUS)-based convolutional neural network (CNN) model trained to differentiate AIP from PDAC, chronic pancreatitis (CP) and normal pancreas (NP), with sufficient performance to analyse EUS video in real time. DESIGN: A database of still image and video data obtained from EUS examinations of cases of AIP, PDAC, CP and NP was used to develop a CNN. Occlusion heatmap analysis was used to identify sonographic features the CNN valued when differentiating AIP from PDAC. RESULTS: From 583 patients (146 AIP, 292 PDAC, 72 CP and 73 NP), a total of 1 174 461 unique EUS images were extracted. For video data, the CNN processed 955 EUS frames per second and was: 99% sensitive, 98% specific for distinguishing AIP from NP; 94% sensitive, 71% specific for distinguishing AIP from CP; 90% sensitive, 93% specific for distinguishing AIP from PDAC; and 90% sensitive, 85% specific for distinguishing AIP from all studied conditions (ie, PDAC, CP and NP). CONCLUSION: The developed EUS-CNN model accurately differentiated AIP from PDAC and benign pancreatic conditions, thereby offering the capability of earlier and more accurate diagnosis. Use of this model offers the potential for more timely and appropriate patient care and improved outcome.

Neoplasia Detection Rate in Barrett's Esophagus and Its Impact on Missed Dysplasia: Results from a Large Population-Based Database.

BACKGROUND & AIMS: It is a challenge to detect dysplasia in Barrett's esophagus (BE) and esophageal adenocarcinomas (EACs) are missed in 25%-33% of cases. The neoplasia detection rate (NDR), defined as the rate of high-grade dysplasia (HGD) or EAC detection during initial surveillance endoscopy, has been proposed as a quality metric for endoscopic evaluation of patients with BE. However, current estimates are from referral center cohorts, which might overestimate NDR. Effects on rates of missed dysplasia are also unknown. We analyzed data from a large cohort of patients with BE to estimate the NDR and factors associated with it, and assess the effects of the NDR on the rate of missed dysplasia. METHODS: We analyzed data from 1066 patients in the Rochester Epidemiology Project-linked medical record system, a population-based cohort of patients with BE (confirmed by review of the endoscopic and histologic reports) from 11 southeastern Minnesota counties from 1991 through 2019. Biopsies reported to contain dysplasia were confirmed by expert gastrointestinal pathologists. The NDR was calculated as the rate of HGD or EAC detected by histologic analyses of biopsies collected during the first surveillance endoscopy. Patients without HGD or EAC at their initial endoscopy (n = 391) underwent repeat endoscopy within 12 months; HGD or EAC detected at the repeat endoscopy were considered to be missed on index endoscopy. Factors associated with NDR and missed dysplasia were identified using univariate and multivariate logistic regression models. RESULTS: The NDR was 4.9% (95% CI, 3.8-6.4); 3.1% of patients had HGD, 1.8% had EAC, and 10.6% had low-grade dysplasia. Factors associated with higher rates of detection of neoplasia included older age, male sex, smoking, increasing length of BE, and surveillance endoscopies by gastroenterologists. This NDR was associated with a substantially lower rate of missed dysplasia (13%). CONCLUSIONS: In an analysis of 1066 patients with BE in a population-based cohort, we found a lower NDR and lower rate of missed dysplasia than previously reported. NDR may have value as a quality metric in BE surveillance if validated in other cohorts.

Outcomes of radiofrequency ablation by manual versus self-sizing circumferential balloon catheters for the treatment of dysplastic Barrett's esophagus: a multicenter comparative cohort study.

BACKGROUND AND AIMS: Radiofrequency ablation (RFA) is the preferred ablative modality for treating dysplastic Barrett's esophagus. The recently introduced self-sizing circumferential ablation catheter eliminates the need for a sizing balloon. Although it enhances efficiency, outcomes have not been compared with the previous manual-sizing catheter. We evaluated the comparative safety and efficacy of these 2 ablation systems in a large, multicenter cohort. METHODS: Patients undergoing RFA at 3 tertiary care centers from 2005 to 2018 were included. Circumferential RFA was performed in a standard fashion, followed by focal RFA as needed. Outcomes were compared between the self-sizing and manual-sizing groups. The primary outcome was the rate of adverse events, including strictures, perforation, and bleeding. Secondary outcomes were procedure time and treatment efficacy, as assessed by rates and time to complete eradication of dysplasia (CE-D) and intestinal metaplasia (CE-IM). RESULTS: Three hundred eighteen patients were included, 90 (28.3%) treated with the self-sizing catheter and 228 (71.7%) with the manual-sizing catheter. Twenty-one patients (6.6%) developed strictures (8 [8.9%] in the self-sizing group and 13 [5.7%] in the manual-sizing group, P = .32). Of the self-sizing strictures, 75% occurred at the 12J dose before widespread adoption of the current 10J treatment standard. One patient developed bleeding, and no perforations were encountered. Procedure time was significantly shorter in the self-sizing group. No significant differences were observed in rates of and time to CE-D and CE-IM. CONCLUSIONS: These findings suggest that both systems are comparable in safety and efficacy. The use of the self-sizing system may enhance the efficiency of RFA for treating dysplastic Barrett's esophagus.

Prospective development and validation of a volumetric laser endomicroscopy computer algorithm for detection of Barrett's neoplasia.

BACKGROUND AND AIMS: Volumetric laser endomicroscopy (VLE) is an advanced imaging modality used to detect Barrett's esophagus (BE) dysplasia. However, real-time interpretation of VLE scans is complex and time-consuming. Computer-aided detection (CAD) may help in the process of VLE image interpretation. Our aim was to train and validate a CAD algorithm for VLE-based detection of BE neoplasia. METHODS: The multicenter, VLE PREDICT study, prospectively enrolled 47 patients with BE. In total, 229 nondysplastic BE and 89 neoplastic (high-grade dysplasia/esophageal adenocarcinoma) targets were laser marked under VLE guidance and subsequently underwent a biopsy for histologic diagnosis. Deep convolutional neural networks were used to construct a CAD algorithm for differentiation between nondysplastic and neoplastic BE tissue. The CAD algorithm was trained on a set consisting of the first 22 patients (134 nondysplastic BE and 38 neoplastic targets) and validated on a separate test set from patients 23 to 47 (95 nondysplastic BE and 51 neoplastic targets). The performance of the algorithm was benchmarked against the performance of 10 VLE experts. RESULTS: Using the training set to construct the algorithm resulted in an accuracy of 92%, sensitivity of 95%, and specificity of 92%. When performance was assessed on the test set, accuracy, sensitivity, and specificity were 85%, 91%, and 82%, respectively. The algorithm outperformed all 10 VLE experts, who demonstrated an overall accuracy of 77%, sensitivity of 70%, and specificity of 81%. CONCLUSIONS: We developed, validated, and benchmarked a VLE CAD algorithm for detection of BE neoplasia using prospectively collected and biopsy-correlated VLE targets. The algorithm detected neoplasia with high accuracy and outperformed 10 VLE experts. (The Netherlands National Trials Registry (NTR) number: NTR 6728.).